Enhanced piecewise least squares approach for diagnosis of ill-conditioned multistation assembly with compliant parts

Abstract

Assembly process-induced dimensional variation has a significant impact on product quality and functionality. The complexity of modern products coupled with part compliance/flexibility frequently results in ill-conditioned assembly systems further adding to the challenges of process control and fault failure diagnosis. This paper proposes an enhanced piecewise least squares (EPLS)-based approach for dimensional fault failure diagnosis of ill-conditioned multistation assembly systems. In this approach, predetermined fault patterns derived from an inverse stiffness matrix of assembly structures and fault patterns obtained from product measurement data are used to detect and isolate dimensional failures caused by fixturing error(s). The EPLS-based diagnostic methodology searches for a set of components called latent vectors with thesearch constrained by assembly response function, the stream of variation analysis (SOVA) model of an assembly system, which performs decomposition of response based on the end-of-line measurements. The verification of the proposed methodology is conducted based on a beam-based model of a multistation assembly process with compliant parts and includes diagnosis of both single and multiple fault scenarios.